Evidence map›Paper›PMID 41900237›Full record

ArticleMicromachines2026

UV Laser Micromachining of FR-4-Based Rigid-Flex PCBs: Predictive Modeling of Penetration Depth Through Design of Experiments.

Giorgio Pellei, Paolo Di Stefano, Luca Mascalchi, Renzo Centi

Abstract read
In one paragraph

Article in Micromachines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. COMicromachines · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Giorgio PelleiDepartment of Industrial and Computer Engineering and Economics, University of L'Aquila, 67100 L'Aquila, Italy.ORCID 0009-0009-1472-8215
Paolo Di StefanoDepartment of Industrial and Computer Engineering and Economics, University of L'Aquila, 67100 L'Aquila, Italy.ORCID 0000-0001-5003-2084
Luca MascalchiElco S.p.A., 67061 Carsoli, Italy.
Renzo CentiElco S.p.A., 67061 Carsoli, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study developed predictive mathematical models for UV laser penetration depth in FR-4-based rigid-flex printed circuit boards, addressing the critical need for precise material removal in applications like protective plug removal. Utilizing a comprehensive Design of Experiments framework, specifically two-level full factorial designs, the influence of key operational parameters-number of loops, scanning speed, and focal position offset-on material removal was systematically investigated in both laminate and multilayer substrates. Empirical models were established for both substrate types, identifying significant factors and interactions that govern penetration depth with physical justification. Comparative analysis revealed that the multilayer model consistently predicted deeper penetration (6-17 µm) than the laminate model under identical conditions, primarily due to reduced heat-associated phenomena with prepreg, yet the laminate model offered a reasonable approximation for complex stack-ups. Rigorous validation through confirmation experiments, achieving 100% success in electrical integrity tests with compliant plug removal, unequivocally demonstrated the models' robustness and reliability. This research provided a crucial tool for optimizing UV laser micromachining processes, significantly reducing parameter identification times and minimizing scrap generation, thereby enhancing the efficiency and reliability of advanced rigid-flex PCB manufacturing.

Indexed as

design of experimentsFR-4material removalpenetration depthpredictive modelingprocess optimizationrigid–flex PCBsUV laser micromachining

Identifiers

PMID41900237
PMCPMC13028538

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.